The present disclosure relates generally to process mining and more specifically to process models that are generated using biased process mining.
Process mining is a relatively young discipline which combines computational intelligence, data mining, process modeling and analysis at the same time. Process mining allows for the analysis of business processes based on recorded information. The basic idea is to extract knowledge from what is recorded by an information system. The information that is recorded is referred to as events. Each event may refer to an activity. Events can include a variety of activities such as withdrawing cash from an automated machine, applying on line for a home equity loan, or even receiving an electronic ticket for a concert on-line.
Each of these recorded events provides data related to the event and so it is appropriately referenced as event data. Process mining's objective is to exploit the recorded event data in a meaningful way so that it provides insight in developing a business process and identify potential problems and bottlenecks. Recorded events can also be referred to as event logs. In conventional process mining using event logs, it is assumed that there is a possible way to sequentially record events.
Process mining aims at improving processes by providing techniques and tools for discovering process, control data, organizational and social structures. Processes can also be improved by asking process owners to recall the steps involved in performing a certain activity and then recording it in a manner similar to event logs. This latter can be performed by interviewing process owners either directly or using information technology. In either case, the idea of process mining is to discover, monitor and improve over time what is required to establish a process associated with a certain activity. Process mining data pertaining to already completed process mining can be used to help form a control flow and a time perspective that can then be applied to current running processes.